b'Kihyuk Sohn'

research

∙ 08/24/2023

Label Budget Allocation in Multi-Task Learning

The cost of labeling data often limits the performance of machine learni...

0 Ximeng Sun, et al. ∙

research

∙ 06/01/2023

StyleDrop: Text-to-Image Generation in Any Style

Pre-trained large text-to-image models synthesize impressive images with...

1 Kihyuk Sohn, et al. ∙

research

∙ 06/01/2023

Learning Disentangled Prompts for Compositional Image Synthesis

We study domain-adaptive image synthesis, the problem of teaching pretra...

0 Kihyuk Sohn, et al. ∙

research

∙ 02/15/2023

Video Probabilistic Diffusion Models in Projected Latent Space

Despite the remarkable progress in deep generative models, synthesizing ...

0 Sihyun Yu, et al. ∙

research

∙ 02/10/2023

MaskSketch: Unpaired Structure-guided Masked Image Generation

Recent conditional image generation methods produce images of remarkable...

0 Dina Bashkirova, et al. ∙

research

∙ 02/06/2023

Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval

In Composed Image Retrieval (CIR), a user combines a query image with te...

0 Kuniaki Saito, et al. ∙

research

∙ 11/30/2022

SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch

Semi-supervised anomaly detection is a common problem, as often the data...

0 Jinsung Yoon, et al. ∙

research

∙ 10/03/2022

Visual Prompt Tuning for Generative Transfer Learning

Transferring knowledge from an image synthesis model trained on a large ...

0 Kihyuk Sohn, et al. ∙

research

∙ 06/02/2022

Prefix Conditioning Unifies Language and Label Supervision

Vision-language contrastive learning suggests a new learning paradigm by...

0 Kuniaki Saito, et al. ∙

research

∙ 05/27/2022

Federated Semi-Supervised Learning with Prototypical Networks

With the increasing computing power of edge devices, Federated Learning ...

0 Woojung Kim, et al. ∙

research

∙ 01/10/2022

Towards Group Robustness in the presence of Partial Group Labels

Learning invariant representations is an important requirement when trai...

0 Vishnu Suresh Lokhande, et al. ∙

research

∙ 12/21/2021

Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types

We introduce anomaly clustering, whose goal is to group data into semant...

22 Kihyuk Sohn, et al. ∙

research

∙ 07/30/2021

Object-aware Contrastive Learning for Debiased Scene Representation

Contrastive self-supervised learning has shown impressive results in lea...

3 Sangwoo Mo, et al. ∙

research

∙ 06/14/2021

Controlling Neural Networks with Rule Representations

We propose a novel training method to integrate rules into deep learning...

0 Sungyong Seo, et al. ∙

research

∙ 06/11/2021

Self-Trained One-class Classification for Unsupervised Anomaly Detection

Anomaly detection (AD), separating anomalies from normal data, has vario...

0 Jinsung Yoon, et al. ∙

research

∙ 06/08/2021

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

We extend semi-supervised learning to the problem of domain adaptation t...

0 David Berthelot, et al. ∙

research

∙ 04/08/2021

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

We aim at constructing a high performance model for defect detection tha...

9 Chun-Liang Li, et al. ∙

research

∙ 02/18/2021

CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

Semi-supervised learning on class-imbalanced data, although a realistic ...

0 Chen Wei, et al. ∙

research

∙ 11/04/2020

Learning and Evaluating Representations for Deep One-class Classification

We present a two-stage framework for deep one-class classification. We f...

0 Kihyuk Sohn, et al. ∙

research

∙ 10/17/2020

i-Mix: A Strategy for Regularizing Contrastive Representation Learning

Contrastive representation learning has shown to be an effective way of ...

4 Kibok Lee, et al. ∙

research

∙ 07/14/2020

Improving Face Recognition by Clustering Unlabeled Faces in the Wild

While deep face recognition has benefited significantly from large-scale...

18 Aruni RoyChowdhury, et al. ∙

research

∙ 05/10/2020

A Simple Semi-Supervised Learning Framework for Object Detection

Semi-supervised learning (SSL) has promising potential for improving the...

3 Kihyuk Sohn, et al. ∙

research

∙ 02/26/2020

Towards Universal Representation Learning for Deep Face Recognition

Recognizing wild faces is extremely hard as they appear with all kinds o...

8 Yichun Shi, et al. ∙

research

∙ 01/21/2020

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Semi-supervised learning (SSL) provides an effective means of leveraging...

7 Kihyuk Sohn, et al. ∙

research

∙ 11/22/2019

Adversarial Learning of Privacy-Preserving and Task-Oriented Representations

Data privacy has emerged as an important issue as data-driven deep learn...

0 Taihong Xiao, et al. ∙

research

∙ 11/21/2019

ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring

We improve the recently-proposed "MixMatch" semi-supervised learning alg...

24 David Berthelot, et al. ∙

research

∙ 06/05/2019

Adaptation Across Extreme Variations using Unlabeled Domain Bridges

We tackle an unsupervised domain adaptation problem for which the domain...

4 Shuyang Dai, et al. ∙

research

∙ 04/16/2019

Active Adversarial Domain Adaptation

We propose an active learning approach for transferring representations ...

16 Jong-Chyi Su, et al. ∙

research

∙ 01/16/2019

Domain Adaptation for Structured Output via Discriminative Representations

Predicting structured outputs such as semantic segmentation relies on ex...

10 Yi-Hsuan Tsai, et al. ∙

research

∙ 01/16/2019

Domain Adaptation for Structured Output via Discriminative Patch Representations

Predicting structured outputs such as semantic segmentation relies on ex...

6 Yi-Hsuan Tsai, et al. ∙

research

∙ 03/23/2018

Feature Transfer Learning for Deep Face Recognition with Long-Tail Data

Real-world face recognition datasets exhibit long-tail characteristics, ...

0 Xi Yin, et al. ∙

research

∙ 02/28/2018

Joint Pixel and Feature-level Domain Adaptation in the Wild

Recent developments in deep domain adaptation have allowed knowledge tra...

0 Luan Tran, et al. ∙

research

∙ 02/28/2018

Learning to Adapt Structured Output Space for Semantic Segmentation

Convolutional neural network-based approaches for semantic segmentation ...

0 Yi-Hsuan Tsai, et al. ∙

research

∙ 08/07/2017

Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos

Despite rapid advances in face recognition, there remains a clear gap be...

0 Kihyuk Sohn, et al. ∙

research

∙ 06/12/2017

Channel-Recurrent Variational Autoencoders

Variational Autoencoder (VAE) is an efficient framework in modeling natu...

0 Wenling Shang, et al. ∙

research

∙ 02/10/2017

Reconstruction-Based Disentanglement for Pose-invariant Face Recognition

Deep neural networks (DNNs) trained on large-scale datasets have recentl...

0 Xi Peng, et al. ∙

research

∙ 03/16/2016

Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units

Recently, convolutional neural networks (CNNs) have been used as a power...

0 Wenling Shang, et al. ∙

research

∙ 12/02/2015

Attribute2Image: Conditional Image Generation from Visual Attributes

This paper investigates a novel problem of generating images from visual...

0 Xinchen Yan, et al. ∙

research

∙ 04/13/2015

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

Object detection systems based on the deep convolutional neural network ...

0 Yuting Zhang, et al. ∙

research

∙ 06/27/2012

Learning Invariant Representations with Local Transformations

Learning invariant representations is an important problem in machine le...

0 Kihyuk Sohn, et al. ∙

Kihyuk Sohn

Featured Co-authors